= Attendees =

Selina, Zach, Scooter, Greg, Eric, Elaine, Tom Ferrin

June 6, 2024

= Agenda =

* journal club (Elaine): [http://www.cgl.ucsf.edu/home/meng/jclub/rin-embedding.html#/ EncoderMap embedding residue interaction networks from trajectories]

= Discussion Notes =
* Wynton upgrades
  * UCSD got money for GPUs but other UCs can't use them
    * they're hosted at SDSC which has painful security requirements
  * Wynton is getting two new GPU nodes that the RBVI will get access to
    * 8 x L40 48GB GPUs to be added to preexisting A40s
    * Officially NRNB nodes but RBVI is a member of NRNB
    * UCSD and Toronto people may also use them
  * Plato is also getting a memory upgrade (2 nodes go up to 1TB)
* 1.8 release
  * Nearly ready, Eric needs to track down tricky GC bug
* Tom gave LookSee demo with his wife, was successful
  * New intern, in the middle of finals, may be here next Thursday
* Elaine presentation
  * Embedding Residue Interaction Networks
    * No training required, uses autoencoder
    * Handles many time points efficiently
  * Motivation
    * Hard to get meaning from MD trajectories, many variables
    * Typically need expert knowledge to know which variables to pay attention to
    * RINs capture structural changes on many scales
    * Prior analyses used averages or only a few snapshots, lost temporal information
  * Implementation
    * Edge drawn b/t non-sequence-adjacent residues with any atoms within 6 angstroms
    * residue closeness centrality --> N-dimensional feature vector for each time point
  * Small Protein Example: Trp-Cage (20 residues)
    * Super long simulation (208 microseconds, >1M frames)
  * Supp data: Trp-Cage PCA and UMAP
    * Author's note: compared to EncoderMap, PCA more global, UMAP more local
  * Multidomain Example: FAT10 (165 residues)
    * FAT10 has two ubiquitin-like domains wiht a flexible linker and tails
    * 50 simulations, 50ns
    * Authors hypothesize that different closed conformations offer different interaction sites for FAT10's many binding partners
    * Map resolves functionally different conformations of a complex system over time, yet retains an interpretable global organization
  * Alternative Featurizations
    * Adjacency matrix colored by density or R_g
    * Degree centrality colored by density or R_g
    * Backbone dihedral angles colored by density or R_g
  * Discussion
    * Protein graph can be defined at different resolutions/with different criteria
    * RINs cannot distinguish fine local changes, best for systems that undergo strong changes
= Action Items =